Senior Data Engineer, Microsoft Fabric & AI

Role Summary

GS1 Global Office is seeking a Senior Data Engineer, Microsoft Fabric & AIto join the Business Intelligence and Data Analytics team. The role will design, build and support secure, scalable enterprise data and analytics solutions using Microsoft Fabric, Azure data services and Power BI. It will also develop AI-enabled data pipelines that connect approved enterprise data with large language model platforms, including Claude AI and other approved enterprise AI tooling.

This is a senior, hands-on individual-contributor role combining data engineering, business intelligence, AI-enabled solution development and production support. The role will provide technical leadership, establish reusable engineering standards and work with stakeholders across GS1 Global Office and Member Organisations to translate business needs into reliable, well-governed data products.

• Design,developandmaintainscalabledatapipelinesusing
  
  Microsoft Fabricand Azuredataservices .
• Buildandsupport Lakehouse,Data Warehouseandsemanticmodelsolutionsusingappropriatearchitectureandreusabledesignpatterns.
• Developreliabledataintegrationprocessesusing Microsoft Fabric Data Pipelines ,Azure Data Factory ,APIsandotherapprovedintegrationmethods.
• Supportmigrationandmodernisationinitiativesinvolving Microsoft Fabricand Azureanalyticsservices .
• Optimisedataprocessingperformance,scalability,maintainabilityandcostefficiency.
• Contributetoenterprisedataarchitecturedecisionsandtheevolutionofshareddatamodelsandanalyticsstandards.

• Design and build retrieval-augmented generation pipelines that securely connect approved enterprise data to Claude AI and other approved large language model platforms.
• Developdatapreparation,chunking,embedding,indexingandretrievalprocessesthatsupportaccurateandtraceableAI-enabledsearchandanalytics.
• SupporttheresponsibleadoptionofapprovedenterpriseAIanddevelopmenttools,including
  
  ClaudeAIand Claude Code ,withsecure data-handlingandaccesspatterns.
• Evaluate and prototype AI-enabled analytics use cases with senior leaders and technical stakeholders, considering business value, architecture, risk and guardrails.
• MonitorandimproveAIpipelinereliability,quality,performanceandcostinlinewithGS1governancestandards.
• Maintainappropriatedocumentation,traceabilityandhumanoversightforAI-enabled solutions.

• Developandmaintain
  
  Power BIdashboards,reports,datasetsandsemanticmodelsthatprovideclearandactionableinsights.
• Supportoperationalreporting,data-qualityreportingandprioritisedadhocanalyticsrequests.
• Workwithbusinessstakeholderstounderstandrequirements,defineacceptancecriteriaandtranslateneedsintosustainablereportingsolutions.
• Promoteconsistentdefinitions,measuresandreportingpracticesacrosstheorganisation.

• Implementdatavalidation,observability,monitoringandqualitycontrolsacrossdatapipelinesandanalyticssolutions.
• Supportmetadatamanagement,data lineage,documentation,retentionandgovernancerequirements.
• DesignsolutionsinaccordancewithGS1informationsecurity,data privacy,access-controlandresponsibleAIrequirements.
• Implement role-basedaccesscontrols,dataclassificationandauditabilityappropriatetothesensitivityandintendeduseofthedata.
• Identifyandescalatedata-quality,security,privacy,model-riskandgovernanceconcerns.

• Developautomatedtestingandvalidationfordatapipelines,semantic models,reportsandAI-enabled solutions.
• Use Git,source control,CI/CDandenvironment-managementpracticestosupportreliabledeploymentacrossdevelopment,testandproductionenvironments.
• Monitor critical solutions, resolve production incidents and troubleshoot pipeline failures, refresh errors, reporting issues, RAG pipeline errors and performance bottlenecks.
• Conduct root-causeanalysisandimplementpreventativeimprovements.
• Maintain technical documentation, operational runbooks and recovery procedures, and contribute to release validation and business-continuity activities.

• Providetechnicalleadershipondataarchitecture,solution design,engineeringstandardsandresponsibleAIimplementation.
• Reviewcodeandsolutiondesigns,shareknowledgeandcoachotherteammembersindataengineeringandanalyticspractices.
• Partner with Product Owners ,Data Engineers,QA,Software Engineeringandbusinessstakeholdersacrossagloballydistributedorganisation.
• Communicatetechnicaloptions,dependencies,risksandcostsclearlytotechnicalandnon-technical audiences.
• Assess trade-offs and make…